Coronavirus-mimicking nanoparticles (CorNPs) in artificial saliva droplets and nanoaerosols: Influence of shape and environmental factors on particokinetics/particle aerodynamics.

Severe acute respiratory syndrome coronavirus 2, abbreviated as SARS-CoV-2, has been associated with the transmission of infectious COVID-19 disease through breathing and speech droplets emitted by infected carriers including asymptomatic cases. As part of SARS-CoV-2 global pandemic preparedness, we studied the transmission of aerosolized air mimicking the infected person releasing speech aerosol with droplets containing CorNPs using a vibrating mesh nebulizer as human patient simulator. Generally speech produces nanoaerosols with droplets of <5 µm in diameter that can travel distances longer than 1 m after release. It is assumed that speech aerosol droplets are a main element of the current Corona virus pandemic, unlike droplets larger than 5 m, which settle down within a 1 m radius. There are no systemic studies, which take into account speech-generated aerosol/droplet experimental validation and their aerodynamics/particle kinetics analysis. In this study, we cover these topics and explore role of residual water in aerosol droplet stability by exploring drying dynamics. Furthermore, a candle experiment was designed to determine whether air pollution might influence respiratory virus like nanoparticle transmission and air stability.

Interfacial Water in the SARS Spike Protein: Investigating the Interaction with Human ACE2 Receptor and In Vitro Uptake in A549 Cells.

The severity of global pandemic due to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has engaged the researchers and clinicians to find the key features triggering the viral infection to lung cells. By utilizing such crucial information, researchers and scientists try to combat the spread of the virus. Here, in this work, we performed in silico analysis of the protein-protein interactions between the receptor-binding domain (RBD) of the viral spike protein and the human angiotensin-converting enzyme 2 (hACE2) receptor to highlight the key alteration that happened from SARS-CoV to SARS-CoV-2. We analyzed and compared the molecular differences between spike proteins of the two viruses using various computational approaches such as binding affinity calculations, computational alanine, and molecular dynamics simulations. The binding affinity calculations showed that SARS-CoV-2 binds a little more firmly to the hACE2 receptor than SARS-CoV. The major finding obtained from molecular dynamics simulations was that the RBD-ACE2 interface is populated with water molecules and interacts strongly with both RBD and ACE2 interfacial residues during the simulation periods. The water-mediated hydrogen bond by the bridge water molecules is crucial for stabilizing the RBD and ACE2 domains. Near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) confirmed the presence of vapor and molecular water phases in the protein-protein interfacial domain, further validating the computationally predicted interfacial water molecules. In addition, we examined the role of interfacial water molecules in virus uptake by lung cell A549 by binding and maintaining the RBD/hACE2 complex at varying temperatures using nanourchins coated with spike proteins as pseudoviruses and fluorescence-activated cell sorting (FACS) as a quantitative approach. The structural and dynamical features presented here may serve as a guide for developing new drug molecules, vaccines, or antibodies to combat the COVID-19 pandemic.

Investigation of the Associations between a Nanomaterial's Microrheology and Toxicology.

With the advent of Nanotechnology, the use of nanomaterials in consumer products is increasing on a daily basis, due to which a deep understanding and proper investigation regarding their safety and risk assessment should be a major priority. To date, there is no investigation regarding the microrheological properties of nanomaterials (NMs) in biological media. In our study, we utilized in silico models to select the suitable NMs based on their physicochemical properties such as solubility and lipophilicity. Then, we established a new method based on dynamic light scattering (DLS) microrheology to get the mean square displacement (MSD) and viscoelastic property of two model NMs that are dendrimers and cerium dioxide nanoparticles in Dulbecco's Modified Eagle Medium (DMEM) complete media at three different concentrations for both NMs. Subsequently, we established the cytotoxicological profiling using water-soluble tetrazolium salt-1 (WST-1) and a reactive oxygen species (ROS) assay. To take one step forward, we further looked into the tight junction properties of the cells using immunostaining with Zonula occluden-1 (ZO-1) antibodies and found that the tight junction function or transepithelial resistance (TEER) was affected in response to the microrheology and cytotoxicity. The quantitative polymerase chain reaction (q-PCR) results in the gene expression of ZO-1 after the 24 h treatment with NPs further validates the findings of immunostaining results. This new method that we established will be a reference point for other NM studies which are used in our day-to-day consumer products.

Emerging cold plasma treatment and machine learning prospects for seed priming: a step towards sustainable food production.

Seeds are vulnerable to physical and biological stresses during the germination process. Seed priming strategies can alleviate such stresses. Seed priming is a technique of treating and drying seeds prior to germination in order to accelerate the metabolic process of germination. Multiple benefits are offered by seed priming techniques, such as reducing fertilizer use, accelerating seed germination, and inducing systemic resistance in plants, which are both cost-effective and eco-friendly. For seed priming, cold plasma (CP)-mediated priming could be an innovative alternative to synthetic chemical treatments. CP priming is an eco-friendly, safe and economical, yet relatively less explored technique towards the development of seed priming. In this review, we discussed in detail the application of CP technology for seed priming to enhance germination, the quality of seeds, and the production of crops in a sustainable manner. Additionally, the combination treatment of CP with nanoparticle (NP) priming is also discussed. The large numbers of parameters need to be monitored and optimized during CP treatment to achieve the desired priming results. Here, we discussed a new perspective of machine learning for modeling plasma treatment parameters in agriculture for the development of synergistic protocols for different types of seed priming.

Sustainable Agriculture through Multidisciplinary Seed Nanopriming: Prospects of Opportunities and Challenges.

The global community decided in 2015 to improve people's lives by 2030 by setting 17 global goals for sustainable development. The second goal of this community was to end hunger. Plant seeds are an essential input in agriculture; however, during their developmental stages, seeds can be negatively affected by environmental stresses, which can adversely affect seed vigor, seedling establishment, and crop production. Seeds resistant to high salinity, droughts and climate change can result in higher crop yield. The major findings suggested in this review refer nanopriming as an emerging seed technology towards sustainable food amid growing demand with the increasing world population. This novel growing technology could influence the crop yield and ensure the quality and safety of seeds, in a sustainable way. When nanoprimed seeds are germinated, they undergo a series of synergistic events as a result of enhanced metabolism: modulating biochemical signaling pathways, trigger hormone secretion, reduce reactive oxygen species leading to improved disease resistance. In addition to providing an overview of the challenges and limitations of seed nanopriming technology, this review also describes some of the emerging nano-seed priming methods for sustainable agriculture, and other technological developments using cold plasma technology and machine learning.

Advances in Smoking Related In Vitro Inhalation Toxicology: A Perspective Case of Challenges and Opportunities from Progresses in Lung-on-Chip Technologies.

The inhalation toxicology of multifaceted particulate matter from the environment, cigarette smoke, and e-cigarette liquid vapes is a major research topic concerning the adverse effect of these items on lung tissue. In vitro air-liquid interface (ALI) culture models hold more potential in an inhalation toxicity assessment. Apropos to e-cigarette toxicity, the multiflavor components of the vapes pose a complex experimental bottleneck. While an appropriate ALI setup has been one part of the focus to overcome this, parallel attention towards the development of an ideal exposure system has pushed the field forward. With the advent of microfluidic devices, lung-on-chip (LOC) technologies show enormous opportunities in in vitro smoke-related inhalation toxicity. In this review, we provide a framework, establish a paradigm about smoke-related inhalation toxicity testing in vitro, and give a brief overview of breathing LOC experimental design concepts. The capabilities with optimized bioengineering approaches and microfluidics and their fundamental pros and cons are presented with specific case studies. The LOC model can imitate the structural, functional, and mechanical properties of human alveolar-capillary interface and are more reliable than conventional in vitro models. Finally, we outline current perspective challenges as well as opportunities of future development to smoking lungs-on-chip technologies based on advances in soft robotics, machine learning, and bioengineering.

Machine-Learning-Based Approach to Decode the Influence of Nanomaterial Properties on Their Interaction with Cells.

In an in vitro nanotoxicity system, cell-nanoparticle (NP) interaction leads to the surface adsorption, uptake, and changes into nuclei/cell phenotype and chemistry, as an indicator of oxidative stress, genotoxicity, and carcinogenicity. Different types of nanomaterials and their chemical composition or "corona" have been widely studied in context with nanotoxicology. However, rare reports are available, which delineate the details of the cell shape index (CSI) and nuclear area factors (NAFs) as a descriptor of the type of nanomaterials. In this paper, we propose a machine-learning-based graph modeling and correlation-establishing approach using tight junction protein ZO-1-mediated alteration in the cell/nuclei phenotype to quantify and propose it as indices of cell-NP interactions. We believe that the phenotypic variation (CSI and NAF) in the epithelial cell is governed by the physicochemical descriptors (e.g., shape, size, zeta potential, concentration, diffusion coefficients, polydispersity, and so on) of the different classes of nanomaterials, which critically determines the intracellular uptake or cell membrane interactions when exposed to the epithelial cells at sub-lethal concentrations. The intrinsic and extrinsic physicochemical properties of the representative nanomaterials (NMs) were measured using optical (dynamic light scattering, NP tracking analysis) methods to create a set of nanodescriptors contributing to cell-NM interactions via phenotype adjustments. We used correlation function as a machine-learning algorithm to successfully predict cell and nuclei shapes and polarity functions as phenotypic markers for five different classes of nanomaterials studied herein this report. The CSI and NAF as nanodescriptors can be used as intuitive cell phenotypic parameters to define the safety of nanomaterials extensively used in consumer products and nanomedicine.

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine.

Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.

Mass spectrometry based analytical quality assessment of serum and plasma specimens with patterns of endo- and exogenous peptides.

Background Inappropriate preanalytical sample handling is a major threat for any biomarker discovery approach. Blood specimens have a genuine proteolytic activity that leads to a time dependent decay of peptidic quality control markers (QCMs). The aim of this study was to identify QCMs for direct assessment of sample quality (DASQ) of serum and plasma specimens. Methods Serum and plasma specimens of healthy volunteers and tumor patients were spiked with two synthetic reporter peptides (exogenous QCMs) and aged under controlled conditions for up to 24 h. The proteolytic fragments of endogenous and exogenous QCMs were monitored for each time point by mass spectrometry (MS). The decay pattern of peptides was used for supervised classification of samples according to their respective preanalytical quality. Results The classification accuracy for fresh specimens (1 h) was 96% and 99% for serum and plasma specimens, respectively, when endo- and exogenous QCMs were used for the calculations. However, classification of older specimens was more difficult and overall classification accuracy decreased to 79%. Conclusions MALDI-TOF MS is a simple and robust method that can be used for DASQ of serum and plasma specimens in a high throughput manner. We propose DASQ as a fast and simple step that can be included in multicentric large-scale projects to ensure the homogeneity of sample quality.